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Statistical mechanics for metabolic networks during steady state growth

Author

Listed:
  • Daniele De Martino

    (Institute of Science and Technology Austria)

  • Anna Andersson

    (Institute of Science and Technology Austria)

  • Tobias Bergmiller

    (Institute of Science and Technology Austria)

  • Călin C. Guet

    (Institute of Science and Technology Austria)

  • Gašper Tkačik

    (Institute of Science and Technology Austria)

Abstract

Which properties of metabolic networks can be derived solely from stoichiometry? Predictive results have been obtained by flux balance analysis (FBA), by postulating that cells set metabolic fluxes to maximize growth rate. Here we consider a generalization of FBA to single-cell level using maximum entropy modeling, which we extend and test experimentally. Specifically, we define for Escherichia coli metabolism a flux distribution that yields the experimental growth rate: the model, containing FBA as a limit, provides a better match to measured fluxes and it makes a wide range of predictions: on flux variability, regulation, and correlations; on the relative importance of stoichiometry vs. optimization; on scaling relations for growth rate distributions. We validate the latter here with single-cell data at different sub-inhibitory antibiotic concentrations. The model quantifies growth optimization as emerging from the interplay of competitive dynamics in the population and regulation of metabolism at the level of single cells.

Suggested Citation

  • Daniele De Martino & Anna Andersson & Tobias Bergmiller & Călin C. Guet & Gašper Tkačik, 2018. "Statistical mechanics for metabolic networks during steady state growth," Nature Communications, Nature, vol. 9(1), pages 1-9, December.
  • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-05417-9
    DOI: 10.1038/s41467-018-05417-9
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    Cited by:

    1. Batista-Tomás, A.R. & De Martino, Andrea & Mulet, Roberto, 2024. "Effective noisy dynamics within the phenotypic space of a growth-rate maximizing population," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 634(C).
    2. Marcelo Rivas-Astroza & Raúl Conejeros, 2020. "Metabolic flux configuration determination using information entropy," PLOS ONE, Public Library of Science, vol. 15(12), pages 1-19, December.

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